Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

Abstract

In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.

Recurrent wavelet neural backstepping controller design with a smooth compensator

“…The neural controller is estimated by an RWNN in (16) where the parameters are adjusted by (46-50). The smooth compensator is designed in (42) where the parameters are adjusted by (52) and (64) for the situations z 2 k k[ d and z 2 k k d, respectively.…”

“…Recently, some researches have devoted to the backstepping control for uncertain nonlinear systems [14][15][16]. This control technique can be effectively used to linearize a nonlinear system in the presence of uncertainties, and it is a systematic and recursive design methodology.…”

Control and synchronization of chaotic systems using a novel indirect model reference fuzzy controller

“…In some of these approaches, the fuzzy system is used as a powerful general function approximator, and different classical methods are used to estimate the parameters of the fuzzy control system. For example, Tao and Taur (2007) proposed a fuzzy sliding mode controller, and Chen et al (2009) investigated a fuzzy identification-based backstepping controller. The model reference adaptive control system is an adaptive servo system, in which the desired performance is expressed in terms of a reference model (Astrom 2008).…”

“…A chaotic system has complex dynamical behaviors that possess some special features, such as excessive sensitivity to initial conditions, broad spectrums of Fourier transform and fractal properties of the motion in phase space (Chen and Dong 1998). Such a phenomena can frequently be observed in natural and engineering systems such as in physics (laser technology and plasma), biology, mechanical engineering and chemistry, and it has been widely studied in literature.…”

Backstepping Adaptive Control of NC Tool Crossbeam Magnetic Levitation System

“…Not only the high speed driving but also the high accuracy supporting and positioning is required by the moving part of the machining center, especially the finish machining under the low speed micro-feed. Therefore the accuracy, rigidity and robustness of the magnetic levitation have been demanded highly [1][2][3].…”